Video Event Recognition by Dempster-Shafer Theory

Xin Hong; Yan Huang; WenJun Ma; Paul Miller; Weiru Liu; Huiyu Zhou

doi:10.3233/978-1-61499-419-0-1031

Video Event Recognition by Dempster-Shafer Theory

Xin Hong, Yan Huang, WenJun Ma, Paul Miller, Weiru Liu, Huiyu Zhou

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

2 Citations (Scopus)

183 Downloads (Pure)

Abstract

This paper presents an event recognition framework, based on Dempster-Shafer theory, that combines evidence of events from low-level computer vision analytics. The proposed method employing evidential network modelling of composite events, is able to represent uncertainty of event output from low level video analysis and infer high level events with semantic meaning along with degrees of belief. The method has been evaluated on videos taken of subjects entering and leaving a seated area. This has relevance to a number of transport scenarios, such as onboard buses and trains, and also in train stations and airports. Recognition results of 78% and 100% for four composite events are encouraging.

Original language	English
Title of host publication	21 European Conference on Artificial Intelligence (ECAI 2014)
Pages	1031-1032
Number of pages	2
DOIs	https://doi.org/10.3233/978-1-61499-419-0-1031
Publication status	Published - Aug 2014
Event	European Conference on Artificial Intelligence (ECAI) - , Czech Republic Duration: 18 Aug 2014 → 22 Aug 2014

Publication series

Name	Frontiers in Artificial Intelligence and Applications

Conference

Conference	European Conference on Artificial Intelligence (ECAI)
Country/Territory	Czech Republic
Period	18/08/2014 → 22/08/2014

Access to Document

10.3233/978-1-61499-419-0-1031

Video Event Recognition by Dempster-Shafer Theory
© 2014 The Authors and IOS Press. This article is published online with Open Access by IOS Press and distributed under the terms of the Creative Commons Attribution Non-Commercial License (https://creativecommons.org/licenses/by-nc/4.0/), which permits use, distribution and reproduction for non-commercial purposes, provided the author and source are cited.
Final published version, 268 KB

R1118ECI: Centre for Secure Information Technologies (CSIT)
McCanny, J. V. (PI), Cowan, C. (CoI), Crookes, D. (CoI), Fusco, V. (CoI), Linton, D. (CoI), Liu, W. (CoI), Miller, P. (CoI), O'Neill, M. (CoI), Scanlon, W. (CoI) & Sezer, S. (CoI)
01/08/2009 → 30/06/2014
Project: Research

Cite this

@inproceedings{2dab9196eb574b8c8c8d56bbbb72111c,

title = "Video Event Recognition by Dempster-Shafer Theory",

abstract = "This paper presents an event recognition framework, based on Dempster-Shafer theory, that combines evidence of events from low-level computer vision analytics. The proposed method employing evidential network modelling of composite events, is able to represent uncertainty of event output from low level video analysis and infer high level events with semantic meaning along with degrees of belief. The method has been evaluated on videos taken of subjects entering and leaving a seated area. This has relevance to a number of transport scenarios, such as onboard buses and trains, and also in train stations and airports. Recognition results of 78% and 100% for four composite events are encouraging.",

author = "Xin Hong and Yan Huang and WenJun Ma and Paul Miller and Weiru Liu and Huiyu Zhou",

year = "2014",

month = aug,

doi = "10.3233/978-1-61499-419-0-1031",

language = "English",

isbn = "978-1-61499-419-0",

series = "Frontiers in Artificial Intelligence and Applications",

pages = "1031--1032",

booktitle = "21 European Conference on Artificial Intelligence (ECAI 2014)",

note = "European Conference on Artificial Intelligence (ECAI) ; Conference date: 18-08-2014 Through 22-08-2014",

}

Hong, X, Huang, Y, Ma, W, Miller, P, Liu, W & Zhou, H 2014, Video Event Recognition by Dempster-Shafer Theory. in 21 European Conference on Artificial Intelligence (ECAI 2014) . Frontiers in Artificial Intelligence and Applications, pp. 1031-1032, European Conference on Artificial Intelligence (ECAI), Czech Republic, 18/08/2014. https://doi.org/10.3233/978-1-61499-419-0-1031

TY - GEN

T1 - Video Event Recognition by Dempster-Shafer Theory

AU - Hong, Xin

AU - Huang, Yan

AU - Ma, WenJun

AU - Miller, Paul

AU - Liu, Weiru

AU - Zhou, Huiyu

PY - 2014/8

Y1 - 2014/8

N2 - This paper presents an event recognition framework, based on Dempster-Shafer theory, that combines evidence of events from low-level computer vision analytics. The proposed method employing evidential network modelling of composite events, is able to represent uncertainty of event output from low level video analysis and infer high level events with semantic meaning along with degrees of belief. The method has been evaluated on videos taken of subjects entering and leaving a seated area. This has relevance to a number of transport scenarios, such as onboard buses and trains, and also in train stations and airports. Recognition results of 78% and 100% for four composite events are encouraging.

AB - This paper presents an event recognition framework, based on Dempster-Shafer theory, that combines evidence of events from low-level computer vision analytics. The proposed method employing evidential network modelling of composite events, is able to represent uncertainty of event output from low level video analysis and infer high level events with semantic meaning along with degrees of belief. The method has been evaluated on videos taken of subjects entering and leaving a seated area. This has relevance to a number of transport scenarios, such as onboard buses and trains, and also in train stations and airports. Recognition results of 78% and 100% for four composite events are encouraging.

U2 - 10.3233/978-1-61499-419-0-1031

DO - 10.3233/978-1-61499-419-0-1031

M3 - Conference contribution

SN - 978-1-61499-419-0

T3 - Frontiers in Artificial Intelligence and Applications

SP - 1031

EP - 1032

BT - 21 European Conference on Artificial Intelligence (ECAI 2014)

T2 - European Conference on Artificial Intelligence (ECAI)

Y2 - 18 August 2014 through 22 August 2014

ER -

Video Event Recognition by Dempster-Shafer Theory

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R1118ECI: Centre for Secure Information Technologies (CSIT)

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